SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

Showing 1230112350 of 17610 papers

TitleStatusHype
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein0
XUAT-Copilot: Multi-Agent Collaborative System for Automated User Acceptance Testing with Large Language Model0
UzBERT: pretraining a BERT model for Uzbek0
V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling0
V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving0
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook0
UzbekTagger: The rule-based POS tagger for Uzbek language0
VAIS ASR: Building a conversational speech recognition system using language model combination0
X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model0
X-VILA: Cross-Modality Alignment for Large Language Model0
UXAgent: A System for Simulating Usability Testing of Web Design with LLM Agents0
Validating the Effectiveness of a Large Language Model-based Approach for Identifying Children's Development across Various Free Play Settings in Kindergarten0
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference0
xVLM2Vec: Adapting LVLM-based embedding models to multilinguality using Self-Knowledge Distillation0
UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns0
UVIS: Unsupervised Video Instance Segmentation0
VALLR: Visual ASR Language Model for Lip Reading0
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes0
VALTEST: Automated Validation of Language Model Generated Test Cases0
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions0
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models0
Yandex School of Data Analysis Russian-English Machine Translation System for WMT140
Utilizing Large Language Models for Natural Interface to Pharmacology Databases0
Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions0
VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search0
Zeroth-Order Optimization Finds Flat Minima0
VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition0
VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding0
Universality and Limitations of Prompt Tuning0
VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models0
Utilizing Large Language Models for Information Extraction from Real Estate Transactions0
Variable Computation in Recurrent Neural Networks0
Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development0
Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling0
Utilizing Dependency Language Models for Graph-based Dependency Parsing Models0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension0
Utilizing ChatGPT to Enhance Clinical Trial Enrollment0
YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection0
Variational Best-of-N Alignment0
Utility-based evaluation metrics for models of language acquisition: A look at speech segmentation0
Utilising a Large Language Model to Annotate Subject Metadata: A Case Study in an Australian National Research Data Catalogue0
Variational Smoothing in Recurrent Neural Network Language Models0
Rethinking the Instruction Quality: LIFT is What You Need0
UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation0
VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding0
VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferences0
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation0
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports0
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified